Research On The Dynamic Path Planning Of Fire Escape In The Building Aided By Intelligent Aircraft

When a fire occurs in a building,trapped personnel cannot quickly find an exit or a safe zone due to various factors such as a blaze,residues and smoke from the fire,and life is seriously threatened.The guidance of intelligent aircraft is very important.In this paper,the traditional A* algorithm cannot deal with the shortcomings of the local path planning of dynamic obstacles.By improving its evaluation function and opening set structure,the A*algorithm can safely avoid the fire,falling residue and heavy concentration caused by the fire environment.Smoke and other dynamic obstacles;for the shortcomings of easy to produce node redundancy and easy to fall into local optimal when dealing with local planning problems after improvement,by improving the weight ratio of the evaluation function and the path generation strategy,the algorithm time and path length are shortened,So that the aircraft can quickly avoid obstacles,reach the target point,and escort life.Finally,by comparing the original A* algorithm with the improved A* algorithm,experiments show that the improved algorithm has higher search efficiency.(1)The traditional A* algorithm is an efficient global path search algorithm,which cannot solve the dynamic local expansion problem.It is improved in its evaluation function and open set structure.The improved local A* algorithm can avoid static and dynamic obstacles.Under the premise of things,quickly and efficiently reach a safe place.(2)In the process of searching the path using the improved local A* algorithm,for the node redundancy brought by the algorithm,after analyzing the different impacts of the evaluation function g(n)and h(n)on the path evaluation,this article Improve the dynamic allocation of weight coefficients of the evaluation function,reduce the redundant points and inflection points of the path,make the path planning faster and smoother,and eliminate the redundant points of path expansion.Experimental simulation results show that the dynamic allocation of improved evaluation function weight coefficients reduces the global(3)programming algorithm time-consuming by 2% and the path length by 7%,effectively improving the efficiency of pathfinding.(4)In order to solve the problem of local optimization caused by the search path,the path generation strategy is improved on the basis of the dynamic evaluation of the weight coefficient of the evaluation function.The experimental simulation results show that the improved algorithm is compared with the original A* algorithm in local planning Time-consuming reduction by 20% and path length by 12.4%.